Strong ground motion data of the 2015 Gorkha Nepal earthquake sequence in the Kathmandu Valley

Strong-motion records of earthquakes are used not only to evaluate the source rupture process, seismic wave propagation and strong ground motion characteristics, but also to provide valuable data for earthquake disaster mitigation. The Kathmandu Valley, Nepal, which is characterised by having soft sediments that have been deposited in an earthquake-prone zone, has experienced numerous earthquakes. We have operated four strong-motion stations in the Kathmandu Valley since 2011. These stations recorded the 2015 magnitude 7.8 Gorkha Nepal earthquake that occurred in the Himalayan continental collision zone. For several months after the mainshock, we deployed four additional temporary stations. Here, we describe the seismic data for 18 earthquakes over magnitude 5.0 collected by this array, including the 2015 magnitude 7.3 Dolakha earthquake of maximum aftershock and three large aftershocks of magnitude 6-class. These data are essential for validating the sedimentary structure of the basin and for evaluating the hazard and risk of future earthquakes in the Kathmandu Valley.

www.nature.com/scientificdata www.nature.com/scientificdata/ Our array in the valley successfully captured strong ground motion records 18 . During this earthquake, other two seismic observation stations located on sediments in the valley also recorded seismic data: the Kantipath station operated by the United States Geological Survey (USGS) 19 , and the station operated by the Department of Mines and Geology, Ministry of Industry 20 . These data are valuable for evaluating strong ground motion characteristics and clarifying the relationship between strong ground motions and damage. For example, the relationship between the building damage ratios based on the visual damage assessments and fragility curves was discussed 21 .
The mainshock was followed by a series of aftershocks. The ensuing aftershock activities were concentrated in the eastern part of the rupture area 22 . Ten days after the mainshock, four additional stations were installed temporarily along a north-south profile of the valley to investigate the distribution of site amplification in the valley using strong-motion records from the aftershocks. Our array captured the aftershocks 23 , including the largest aftershock (M w 7.3) on 12 May 2015 occurred in the Dolakha region 24 (Fig. 1). The strong ground motion distribution for the mainshock was insufficient to estimate the site amplification and wave propagation characteristics for the entire valley. Data from additional temporary stations and strong-motion records of small and mid-sized earthquakes are useful to estimate the site amplification characteristics and validate the velocity structure models. Ichiyanagi et al. 25 investigated aftershock activity, and Bijukchhen et al. 26 constructed the initial velocity structure model for the Kathmandu Valley based on previous geological and geophysical exploration results and the strong-motion records. Mori et al. 27 proposed correction terms for the amplification by the sedimentary layers in the ground motion prediction equation.
The data were disseminated through the figshare data repository at https://doi.org/10.6084/ m9.figshare.19809052 28 . These will prove to be important fundamental data for earthquake disaster mitigation activities in the Kathmandu Valley. For example, these data can be used to produce seismic hazard maps, building damage prediction maps, etc., as well as seismic design of building structures. In this study, we present observation-based strong ground motion data. We introduce our strong-motion array in the Kathmandu Valley and describe the dataset obtained after the 2015 Gorkha earthquake sequence. We also explain the quality of these data.  Figure 2 and Table 1 show the locations of the strong-motion arrays. The geological conditions 29 differed at each of the stations; for example, the KTP site was located on exposed rocky hillocks that breached through the sediments, while the TVU, PTN, THM, BKT, RNB, PPR sites were all located on lake sediments, and the KPN site was located on fluvial sediments of the Bagmati River.  Table 2. www.nature.com/scientificdata www.nature.com/scientificdata/ observation system. The observation equipment was selected to obtain stable records, even under conditions of unstable power supply in Kathmandu. The installed instruments consisted of a highly damped moving coil-type three-component accelerometer (JEP-6A3-2, Mitutoyo Corp., Japan) which does not require a power supply, and a 24 Analog-to-Digital bit low-power data logger (DATAMARK LS-8800, Hakusan Corp., Japan) with an external GPS antenna for time calibration. The sampling rate was 100 Hz and data were recorded continuously at all stations. The accelerometer had a flat response (−3 dB) for ground acceleration from 0.1 Hz to an aliasing frequency with a sensitivity of 0.22 V/m s −2 . The data loggers at the permanent stations were powered by a DC supply and fitted with a 12 V rechargeable car battery along with a voltage stabiliser. The data loggers at the temporary stations were powered by a 12 V car battery. Due to safety considerations and the high population density in the study area, these instruments were installed on the foundation-level floor of reinforced concrete buildings that were one to four stories high, and outside buildings for a few temporary stations. The GPS antennas were either positioned outside of the buildings or at windows so that they could receive satellite signals. The permanent accelerometers were affixed to the floor with bolts, while temporary accelerometers were affixed using two-part epoxy adhesives. Accelerometers were levelled and oriented along the building or to the magnetic north. Recorded data were stored on 16 GB SD cards and data were collected on-site. Maintenance of the permanent stations was performed at six-month intervals. Permanent stations were maintained two weeks before the 2015 Gorkha earthquake.

Strong
Strong ground motion. The strong-motion stations recorded the 2015 Gorkha Nepal earthquake sequence data. Figure 1 and Table 2 show 18 mid-to-large sized earthquakes (5.0 < M < 7.3) with the strong-motion data of signal-to-noise ratios > 2 located within the epicentral area. Source parameters were from the USGS National Earthquake Information Center (NEIC: https://www.usgs.gov/programs/earthquake-hazards/earthquakes). As examples, Fig. 3 shows the acceleration waveforms of the M w 6.7 earthquake ( www.nature.com/scientificdata www.nature.com/scientificdata/

Data records
The data files as shown in Table 2 were deposited in the figshare data repository at https://doi.org/10.6084/ m9.figshare.19809052 28 . Minimum processing included converting the data from raw WIN system 30 format to acceleration waveform data and correcting the orientation. The data files consist of four columns for time and geographic north-south (NS), east-west (EW), up-down (UD) data in ASCII text format. A recording length is set to 180 s before and after the P-and S-waves. Table 3 shows the data format characteristics. The site location and recording start time (UTC) are described in the file header. The file name consists of a code, earthquake date, and file extension (.txt).
Since the JEP-6A3 accelerometer is an over-damped moving coil mechanical seismograph, it is possible to derive accurate long-period ground motions at <0.1 Hz by correcting for the sensor response. The pendulum motion is proportional to the ground velocity, h is a damping constant of 26, and the natural frequency f 0 is 3 Hz. The frequency response function Λ(ω) has an amplitude of 1 and can be estimated as follows: where ω is the angular frequency and ω 0 is the resonance angular frequency of the pendulum. Therefore, the accelerometer had a flat response for ground acceleration over 0.1 Hz.

Technical Validation
After the 25 April 2015 Gorkha earthquake, we checked the permanent strong-motion stations. The instruments were undamaged and the buildings in which the equipment were installed were visually assessed as being either undamaged or only slightly damaged 21 . In addition, continuous recordings without any missing data were obtained throughout the event. However, since Kathmandu is a populous city with numerous buildings and heavy traffic, these sources of artificial ambient noise may have contaminated the observed seismic records. ambient noise. In populated metropolitan areas, noise levels attributed to microtremors generated by human activities such as traffic can be very high. Therefore, we estimated the vibration level from the power spectral density (PSD) of acceleration at each site. As an example, the horizontal vibration levels for one week, from 15 to 21 May 2015, are shown in Fig. 4. Observed continuous records were subdivided into intervals of 40.96 s and the PSD of the horizontal component, its root mean square value, and the hourly mean value were calculated. The vibration levels are compared against a reference acceleration vibration level of 10 −6 m/s 2 with no weighting of the vibration sensation. The vibration levels exhibited daily cycles in the high-frequency range; specifically, the difference in the noise level between day and night differed by approximately 20 dB. The microtremors attributed to human activity affect this frequency range, and the vibration levels measured at the sedimentary stations in the central urban areas were larger than those at KTP station on the hilly rock. Conversely, although the amplitudes of the microseisms were small, predominant low-frequency peaks were observed at sedimentary sites. Figure 5 shows the average PSD for microtremors not containing seismic data over one day at each station. Compared to the results obtained from the high-noise model 31 , which uses the average value of the high background noise power obtained from broadband seismometers, the PSDs recorded by our array were slightly  www.nature.com/scientificdata www.nature.com/scientificdata/ higher than those estimated by this model. Although the noise levels were relatively high, the PSDs for the recorded earthquake data far exceed those estimated using the model. However, the observed data may contain records from multiple earthquakes because recordings were performed immediately after large earthquakes.